bestrag 0.3.3

bestrag: Library for storing and searching document embeddings in a Qdrant vector database using hybrid embedding techniques.

Introducing BestRAG! This Python library leverages a hybrid Retrieval-Augmented Generation (RAG) approach to efficiently store and retrieve embeddings. By combining dense, sparse, and late interaction embeddings, BestRAG offers a robust solution for managing large datasets.

✨ Features

🚀 Hybrid RAG: Utilizes dense, sparse, and late interaction embeddings for enhanced performance.
🔌 Easy Integration: Simple API for storing and searching embeddings.
📄 PDF Support: Directly store embeddings from PDF documents.

🚀 Installation

To install BestRAG, simply run:

pip install bestrag

📦 Usage

Here’s how you can use BestRAG in your projects:

from bestrag import BestRAG

rag = BestRAG(
    url="https://YOUR_QDRANT_URL", 
    api_key="YOUR_API_KEY", 
    collection_name="YOUR_COLLECTION_NAME"
)

# Store embeddings from a PDF
rag.store_pdf_embeddings("your_pdf_file.pdf", "pdf_name")

# Search using a query
results = rag.search(query="your search query", limit=10)
print(results)

# Delete particular pdf embeddings
rag.delete_pdf_embeddings("home/notes.pdf")

Note: Qdrant offers a free tier with 4GB of storage. To generate your API key and endpoint, visit Qdrant.

🤝 Contributing

Feel free to contribute to BestRAG! Whether it’s reporting bugs, suggesting features, or submitting pull requests, your contributions are welcome.

📝 License

This project is licensed under the MIT License.

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Created by samadpls 🎉